Method of Adaptively Predicting Blood-Glucose Level by Collecting Biometric and Activity Data with A User Portable Device

ABSTRACT

A method of adaptively predicting blood-glucose level by collecting biometric and activity data with a user portable device utilizes a portable computing device carried by a user to collect movement data and biometric data about and from the user. Collected data is processed by a blood glucose prediction formula generation algorithm in order to produce multiple blood glucose level prediction formulas. Based on the activity level measured by the device, a corresponding blood glucose prediction formula is used to predict blood glucose levels for a certain period of time. The prediction formulas recursively provide feedback and change for successive iterations and new formulas are generated as new data is collected.

FIELD OF THE INVENTION

The present invention relates generally to health measurements. Moreparticularly, the present invention relates to accurate prediction ofblood glucose levels.

BACKGROUND OF THE INVENTION

Patients with diabetes must constantly monitor their blood glucoselevels and adjust insulin doses to keep their blood glucose levels asclose to normal as possible. When blood glucose levels are out of theirnormal range, serious short-term and long-term complications may occur.Systems that can predict future blood glucose levels can notify thepatient of imminent changes, enabling them to take preventive action.Current blood glucose systems have limited predictive algorithms todetermine future blood glucose values in real-time. Typical closed loopsystems rely on a single algorithm to predict blood glucose levels usingreal-time data. These systems do not factor in the user's dailyactivity, their environment and/or metabolic rate, which makes thecurrent systems blood glucose predictive values valid for only a shorttime period. Current systems do not account for the constantly changingfactors for each unique individual.

Therefore, it is the main objective of the present invention to providea system that is capable of predicting a patient's blood glucose levelwith accuracy for up to 2 hours, accounting for the various changes inactivity of the user. The present invention will utilize a device forcollecting data relating to the blood glucose levels of an individual.The system as a whole will learn the patient's behavior and constantlyderive a new equation for each user based on their daily activity. It isunderstood that each patient is unique and requires a personalizedequation which must be constantly derived to accurately predict theirfuture blood glucose levels. Therefore, it is another objective of thepresent invention to provide a system that uniquely predicts patients'blood glucose levels through personalized formulas that are constantlychanging based on their behavior.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a system diagram with two portable devices.

FIG. 1B is a system diagram with one portable device.

FIG. 2A is a stepwise flow diagram describing the steps of the overallprocess of the present invention.

FIG. 2B is a continuation of FIG. 2A.

FIG. 3 is a stepwise flow diagram specifying details of the overallprocess.

FIG. 4 is a stepwise flow diagram describing steps for using twoportable devices.

FIG. 5 is a stepwise flow diagram describing steps for using oneportable device.

FIG. 6 is a stepwise flow diagram describing steps for collecting usermovement data.

FIG. 7 is a stepwise flow diagram describing steps for collecting userbiometric data.

FIG. 8 is a stepwise flow diagram describing steps for designating aspecific activity level.

FIG. 9 is a stepwise flow diagram describing steps for computing apredictive blood-glucose model.

FIG. 10 is a stepwise flow diagram describing steps for designating apreceding blood-glucose result for an initial iterative blood-glucosecalculation.

FIG. 11 is a stepwise flow diagram describing steps for determining apreceding blood-glucose result for a subsequent iterative calculationfollowing an arbitrary iterative calculation.

FIG. 12 is a stepwise flow diagram describing steps for adjustingcoefficients of the blood-glucose predictive formulas.

FIG. 13 is a flow diagram showing the general process of the presentinvention.

FIG. 14 depicts a graphical representation of the blood glucosepredictive values vs the dataset.

FIG. 15 shows an example blood glucose calculation.

DETAIL DESCRIPTIONS OF THE INVENTION

All illustrations of the drawings are for the purpose of describingselected versions of the present invention and are not intended to limitthe scope of the present invention. The present invention is to bedescribed in detail and is provided in a manner that establishes athorough understanding of the present invention. There may be aspects ofthe present invention that may be practiced without the implementationof some features as they are described. It should be understood thatsome details have not been described in detail in order to notunnecessarily obscure focus of the invention.

The present invention is a method for predicting a patient's bloodglucose level with accuracy up to two hours. The present inventionutilizes a small carrying device that will collect user activity, bloodglucose readings, food intake, insulin injection data and othervariables relevant to blood glucose levels and general fitness. Data isgathered by and inputted to the device, which communicates with a remoteserver in order to perform predictive blood glucose calculations.Essentially, the present invention functions to accurately derive theuser's metabolic rate from a variety of factors, enabling more accurateblood glucose level prediction. A general overview of the process of thepresent invention is shown in FIG. 13.

A computing device such as a cell phone or personal computer is utilizedas a user interface to view blood glucose predictions and input datainto the system. User activity data is continuously collected andcategorized into one of multiple activity levels in small sequentialtime increments. In one embodiment of the present invention, useractivity is categorized in five minute intervals. Periodically, the userwill be alerted at certain times to input various required variable datafor predicting blood glucose levels. Data such as blood glucosereadings, food intake, time of insulin injection and insulin value willbe entered through an external device such as a mobile phone, glucometeror other external device and sent to the carrying device via a wired orwireless connection. In the preferred embodiment of the presentinvention, the carrying device will be capable of taking blood oxygenand pulse rate readings. In one embodiment, a pulse oximeter is to beintegrated into a button so that such readings can be taken when theuser presses a button. General system diagrams with two mobile devicesand one mobile device are shown in FIGS. 1 and 2, respectively.

In the general method of the present invention shown in FIGS. 2A-2B, atleast one remote server is provided (Step A). The remote server servesas the primary computing element of the present invention and manages ablood-glucose (BG) predictive formula generator and storestime-dependent user historical (TDUH) data. The TDUH data is all datathat has been collected for a given user, including, but not limited to,movement data, biometric data, and environmental data, and in generalall data is linked to corresponding times the data was collected. In thepreferred embodiment, the collected data over time is run through apolynomial curve fitting method to generate a predictive model for theuser's future blood glucose levels based on their current activity andmetabolic rate. The predictive formula generator is any collection ofalgorithms, computer code, machine language, or othercomputer-executable instructions which can convert data collected forthe user into BG predictive formulas. Data is run through the predictiveformula generator and outputs are produced as coefficients forpolynomial functions. Various other details of the method are specifiedin FIG. 3.

Furthermore, at least one portable computing device is provided (StepB). Each portable computing device is communicably coupled to the remoteserver. Each portable computing device should have at least one means ofelectronic communication, such as, but not limited to, a wirelesscommunications chipset such as Wi-Fi, Bluetooth or other wirelesscommunications standards, one or more universal serial bus (USB) ports,or other electronic communication means.

In one embodiment, a single portable device is provided as the at leastone computing device. In this embodiment, the single portable devicecomprises all necessary components necessary to facilitate all describedaspects of the at least one portable computing device, such as, but notlimited to, an internal movement sensor, biometric sensors and othersensors, wired or wireless electronic communication abilities necessaryto communicate with the remote server, a user interface, and othercomponents.

In one embodiment, a carriable monitoring device and a mobile computingdevice are provided as the at least one portable computing device. Thecarriable monitoring device is a component with a variety of sensors forcollecting data, indicators and wireless or wired connectioncapabilities to receive and send the collected data. In the preferredembodiment of the present invention, the carriable monitoring devicecomprises an onboard database, a microcontroller, a 3-axis accelerometeror 9-axis Gyro-accelerometer-compass, a pulse rate sensor, a bloodoxygen sensor, an infrared (IR) body temperature sensor, a surfacefinger temperature sensor, an ambient temperature sensor, a bodyimpedance analysis (BIA) or body mass indicator (BMI) reader, a sweat ordehydration sensor, a humidity sensor, a USB connection, a plurality ofbuttons, light indicators and Bluetooth and/or Wi-Fi capabilities. Anambient light sensor on the mobile computing device may furthersupplement the aforementioned components of the carriable monitoringdevice. The mobile computing device is any electronic device which caninterface wired or wirelessly with the carriable monitoring device andwhich has a user interface for the user to view BG prediction data andinput various data. The mobile computing device may be a “smart” cellphone or tablet computer, or laptop computer, or another similar type ofdevice. Preferably, the mobile computing device and the carriablemonitoring device are paired in wireless electronic communicationthrough each other Bluetooth or Wi-Fi.

A set of user activity levels and a set of current BG predictiveformulas is stored on the portable computing device (Step C). Thecurrent BG predictive formulas are generated in a manner to be discussedlater. The method of the present is a repeating process, and the presentinvention is herein described from the standpoint of one or morearbitrary iterations in the process. Each user activity level isassociated with a corresponding formula within the set of current BGpredictive formulas. The user activity levels are pre-defined in thesystem. The user activity levels correspond to different levels of usermovement detected through the portable device. For example, in oneembodiment, the user activity levels comprise six activity levels:stagnant, walk low, walk exercise, jog, run low, and run high. The BGpredictive formulas are transient and change over time as the systemprocesses more data and refines the predictive formulas. The BGpredictive formulas are received by the portable computing device fromthe remote server as they are newly generated. This ensures a moreaccurate representation of the user's activity (as well as otherpertinent factors), rather than having a single formula which may onlyshow a very rudimentary representation of the user's activity. Thecontinuous activity readings allow the device to predict future bloodglucose levels based on their current state, which may be constantlychanging.

For example, if the user's physical activity is drastically changingevery five minutes, the predictive model will adjust based on theiractivity and show a different predictive model for each time periodusing the corresponding baseline formulas. The device will then presentthe predictive model onto a paired mobile device where the user will beable to see what their blood glucose levels will be within a two-hourwindow. The information will preferably be presented in a graph form,where the user can pick and choose a specific time to show thepredictive blood glucose level, in 30 minutes or 1 hour for example.

The user carries the portable computing device, which has an internalmovement sensor, as specified in FIG. 6. User movement data is collectedwith the internal movement sensor of the portable computing device (StepD). The internal movement sensor may be any currently existing or newmovement sensor capable of measuring movement of the portable computingdevice through inertial measurements or other means. In one embodiment,the internal movement sensor is a 3-axis MEMS accelerometer. In oneembodiment, the movement sensor is a 9-axis gyro-accelerometer-compass.In one embodiment, the internal movement sensor is an inertialmeasurement unit (IMU).

The user movement data is associated with a specific activity levelwithin the set of user activity levels with the portable computingdevice (Step E). The portable computing device reads the currentactivity level of the user through the movement sensor and categorizesthe user movement data into a range corresponding with one of the useractivity levels. Referring to FIG. 8, more particularly, a plurality ofmovement ranges is provided as stored on the portable computing device,wherein each movement range is associated to a corresponding activitylevel within the set of user activity levels. The user movement data iscompared to each movement range with the portable computing device inorder to identify a matching range from the plurality of movementranges, and a corresponding activity level is designated as the specificactivity level. In general, signals received from the movement sensorwith higher intensity and/or frequency will be associated with higheruser activity levels and vice versa. Alternatively stated, for eachfive-minute data collection period, the user's activity is categorizedinto a distinct numerical value representing the level of activity. Thisnumerical value will be stored in the device's onboard database to besent to the remote server to calculate a baseline formula with the BGpredictive formula generator. The baseline formulas depict the varyinglevels of activity. For example, if there are 6 distinct activity levelsfor the user, there will be 6 baseline formulas representing eachactivity level.

A BG predictive model is extrapolated from the corresponding formula ofthe specific activity level over a pre-defined time block with theportable computing device (Step F). FIG. 14 shown a graphicalrepresentation of an example BG predictive model versus a dataset. It isthe intent of the present invention to generate BG predictive modelsthat are accurate for up to two hours from the point of generation, thesaid two hours being a specific example of the pre-defined time block.The BG predictive model is then displayed through the portable computingdevice (Step G). More specifically, in the preferred embodiment the BGpredictive model is visually displayed as a graphical plot through theportable computing device on a display device of the portable computingdevice. The BG predictive model displayed on the portable computingdevice is dependent on which of the activity levels the portablecomputing device is currently detecting. For example, the BG predictivemodel will predict the user's BG level to drop faster if the user is ata high activity level as opposed to a low activity level.

Steps D through G are repeated constantly as a plurality of iterationsuntil the remote server updates the portable computing device with a setof new BG predictive formulas (Step H), with the process of steps Dthrough G subsequently repeating with the new BG predictive formulas.The user movement data for each iteration is compiled intotime-dependent user movement (TDUM) data. The TDUM data is thecollection of user movement data in relation with time. The TDUM datarecords at which points in time the user was at which user activitylevels. In the preferred embodiment, each of the plurality of iterationsis executed at a pre-defined time interval. For example, each of theplurality of iterations is executed at a five minute interval. Thus,every five minutes, the portable computing device identifies the user'sactivity level during the previous five minutes and computes the BGpredictive model for the corresponding formula of the activity level ofthe previous five minutes. If the user switches activity levels betweeniterations, the BG predictive model for the new iteration will bedifferent than the previous BG model due to using different BGpredictive formulas to calculate them, corresponding with the differentactivity levels. In the preferred embodiment, each BG predictive modelis extrapolated with the assumption that the user's activity level willnot change. If the user's activity level changes, the BG predictivemodel will change with the assumption that the new activity level willpersist.

The pre-defined time block of the BG predictive model is a multiple ofthe pre-defined time interval of the plurality of iterations. Knowingthe pre-defined time interval of the iterations, the pre-defined timeblock of the BG predictive model can be achieved by specifying a numberof iterations to complete in order to achieve the BG predictive modelover the pre-defined time block. For example, if the pre-defined timeinterval of the iterations is specified as five minutes, then in orderto specify the pre-defined time block as two hours, 24 iterations mustbe completed.

While the said iterations of user movement data collection and BGpredictive modeling are occurring, time dependent user biometric (TDUB)data is additionally collected with the portable computing device (StepI). The TDUB data should be understood to be any data pertaining to theuser other than movement which may be useful for calculating andpredicting the user's BG level. Referring to FIG. 7, in one embodiment,a plurality of biometric sensors is provided with the portable computingdevice, and an automatically-collected portion of the TDUB data arereceived with the plurality of biometric sensors. For example, a pulsemonitor may be attached to the user at all times, and the user's pulserate would belong to the automatically-collected portion of the TDUBdata.

In one embodiment, a user interface is provided with the portablecomputing device, and manually-inputted portions of the TDUB data arereceived through the user interface. The manually-inputted portions ofthe TDUB data may include, but are not limited to, current BG level,food intake, and insulin injection time and value. In the preferredembodiment, the user is prompted through the portable computing deviceto input various required data for predicting blood glucose levels. Forexample, if the TDUH indicates that the user typically eats a meal as 2o'clock P.M., and the user does not enter food intake information withina specified threshold after 2 o'clock P.M., the user will be promptedthrough the portable computing device to enter food intake information.Various such reminders and/or alters will be presented to the userthroughout the day to input various required data, such as, but notlimited to, food intake, insulin injection time and value of insulininjection. It is understood that the indications may be presentedthrough blinking lights on the portable computing device, vibrations,sound alerts or a combination of these methods.

In general, the TDUB data includes information selected from a groupconsisting of: current BG level, food intake, insulin injection value,body mass index (BMI), pulse rate, blood oxygenation level, bodyimpedance, and combinations thereof. In some embodiments, environmentaldata may also be collected and included in the TDUH data, such as, butnot limited to, ambient temperature, ambient light level, and ambienthumidity.

Preferably, a pulse oximeter will be integrated into a button of theportable computing device to take pulse rate and blood oxygen readings.Food intake and blood glucose levels will be entered through externaldevices and then transferred over to the portable. The user's bloodglucose level will be taken via a glucometer and wired to the carryingdevice via the USB port to transfer the data. The user will have twooptions in inputting their food intake. Under the first method, the userwill input the amount of food eaten and the sugar content of the food.The food intake levels will fall into one of the following categories:light, medium, normal or heavy; while the sugar content of the meal willfall into one of the following categories: low, medium or high. Underthe second method, the user will take a picture of their meal via themobile device. The image will then be processed by the remote serverwhich will then automatically determine the food type, size and sugarcontent. The insulin injection time and value will also be entered viathe mobile device and transferred over to the carrying device. Theinputted data such as the food intake, blood glucose level, insulininjection, and pulse rate/blood oxygen will be categorized and convertedinto a weighted numerical value.

Subsequently, the TDUM data and the TDUB data are integrated into theTDUH data with the remote server. A set of new BG predictive formulasare then computed with the remote server by inputting the TDUH data intothe BG predictive formula generator (Step K). The set of new BGpredictive formulas may be computed at any time new data is received bythe remote server. In general, the larger data set available to theremote server, the better the BG predictive formulas will be; therefore,it is desirable to compute new BG predictive formulas as often aspossible. Computation of the new predictive formulas may also betriggered by receiving data considered to be important, such as, but notlimited to, current BG level, food intake, or insulin injection. In thepreferred embodiment, the remote server executes a polynomial curvefitting process on the TDUH in order to compute the set of new BGpredictive formulas. In general, the BG predictive formulas will takethe form of:

Ax5+Bx4+Cx3+Dx2+Ex+F

where A, B, C, D, E and F are constants. The constant value F isdiscarded from the final equation, but used to generate the feedbackmultiplier coefficients. The present invention will essentially derivethe user's metabolic rate from a sample set of data, where the highorder coefficients are used to control the user's increasing ordecreasing metabolic rate. In other words, the change in the weightedcoefficients are based on the user's changing metabolic rate.

Referring to FIG. 4, in the embodiment where the at least one portabledevice is provided as a carriable monitoring device and a mobilecomputing device, steps D through F are executed with the carriablemonitoring device, the BG predictive model is sent from the carriablemonitoring device to the mobile computing device prior to step G, step Gis executed with the mobile computing device, and step I is executedwith the carriable monitoring device. Furthermore, the TDUM data is sentfrom the carriable monitoring device to the mobile computing deviceprior to step J, the TDUM data and the TDUB data are sent from themobile computing device to the remote server after step I, and the newBG predictive formulas are sent from the remote server to the carriablemonitoring device through the mobile computing device after step K. Allinformation will be stored in the carrying device and will not be storedin the mobile device. The mobile computing device will only serve thepurpose of facilitating data input as well as displaying data. Atspecific times throughout the day, the collected data will betransmitted to the remote server via a Bluetooth low energy or wirelesslocal area network connection. The microcontroller of the carriablemonitoring device will be responsible for any numerical conversions,choosing the correct numerical values depending on user activity, aswell as sending and receiving information at certain time intervals.

Referring to FIG. 5, in the embodiment where the at least one portabledevice is provided as a single portable computing device, the TDUM andthe TDUB data are sent from the single portable computing device to theremote server after step I, and the new BG predictive formulas are sentfrom the remote server to the single portable computing device afterstep K.

In the preferred embodiment of the present invention, all data collectedfor the user is converted to weighted numerical values and stored to theonboard database. Values such as, but not limited to, food intake level,sugar content of the food intake, measured pulse rate, blood oxygenlevel, and the other various data are categorized into weightednumerical values. The weighted numerical values are sent to the remoteserver to generate a unique baseline BG prediction algorithm for theuser. In other words, the remote server will run an algorithm based onthe various data collected to generate a set of baseline predictive BGformulas used for each predictive blood glucose reading. Thecorresponding baseline formula is then performed using the blood glucosereadings taken when the blood sugar levels have stabilized, typically 1to 1.5 hours after a meal and/or insulin injection. In the preferredembodiment, the remote server produces the predictive BG formulas in theform as coefficients for a polynomial equation as outputs from apolynomial curve fitting method.

The iterative process of steps D through G is an adaptive feedback loop.Each iteration produces a predictive BG value as output, which thesubsequent iteration uses to produce its own predictive output.Additionally, in the preferred embodiment, the independent variable (X)in the BG prediction formulas is an integer which is incremented by onein every iteration. X is bounded and repeats within the bounds, whereinthe X bound is determined by the remote server and fits the real sampleddata by 3% in the preferred embodiment. Furthermore, the coefficients ofthe prediction equations are decremented between iterations. FIG. 15shows a sample calculation.

Referring to FIG. 9, providing a preceding BG result (Step L), a currentcounting variable is applied into the corresponding formula for thespecific activity level in order to calculate a current BG result withthe portable computing device (step M), more specifically the with themicroprocessor of the portable computing device. The current BG resultis modified with the preceding BG result in order to calculate apredictive BG result with the portable computing device (Step N). Thecounting variable is then incremented with the portable computing device(Step 0). Steps L through O are repeated as a plurality of iterativecalculations with the portable computing device in order to compile thepredictive BG result from each iterative calculation into the BGpredictive model (Step P).

Referring to FIG. 10, for a first iterative calculation from theplurality of iterative calculations, a pre-defined initial BG result isprovided. The pre-defined initial BG result will be a value inputted bythe user through the user interface. The pre-defined initial BG resultis designated as the preceding BG result for the first iterativecalculation with the portable computing device.

Referring to FIG. 11, for an arbitrary iterative calculation and asubsequent iterative calculation from the plurality of iterativecalculations, the predictive BG result for the arbitrary iterativecalculation is designated as the preceding BG result for the subsequentiterative calculation with the portable computing device.

Each current BG predictive formula comprises a plurality of polynomialterms, and each polynomial term includes a coefficient. After eachiteration, at least one of the polynomial terms of each current BGpredictive formula is multiplied by a scaling factor and an inverse ofthe current counting variable with the portable computing device. Thisis done in order to scale the corresponding formula for the specificactivity level in between steps M and O.

For example, at the beginning of the third iterative calculation or atthe end of the second iterative calculation, the counting variable willbe incremented from two to three. The value of three will then beinputted into the independent variable of the corresponding formula forthe specific activity level, producing the current BG result as output.The predictive BG result is calculated by adding the preceding BG resultfrom the second iterative calculation and the current BG result. In thepreferred embodiment, a constant is furthermore generated by BGpredictive formula generator of the remote server for each new set of BGpredictive formulas and will be added to the preceding BG result and thecurrent BG result to produce the predictive BG result. The predictive BGresult from the third iterative calculation then becomes the precedingBG result for the fourth iterative calculation, and the countingvariable is incremented from three to four.

Additionally, before the counting variable is incremented from three tofour, one or more of the coefficients of the polynomial terms of thecorresponding formula is multiplied by ⅓ and by a scaling factorcalculated by the remote server. The scaling factor is calculated by theremote server for each new set of BG predictive formulas by thepredictive formula generator as part of an artificial intelligence (AI)learning analytics algorithm. Thus, the system will continuouslygenerate a new predictive model based on the changing user activity.This information will then be displayed on the user's mobile device. Theuser will be presented with a graph depicting their predicted bloodglucose levels over a two-hour time period. The user will be able tochoose a specific time within the two-hour window (i.e. 30 minutes fromnow, 1 hour from now, etc.) to see what their predicted blood glucoselevel will be.

Furthermore, in one embodiment, other data collected by the portablecomputing device such as the environmental temperature, humidity, lightsensed, body mass index (BMI), patient core and skin temperature, etc.will be sent to a physician. The physician will then be able to remotelyreview the data and determine the physical fitness level of the user,which can be indicated on the mobile device. For example, a high pulserate and BMI assigned to a lower numerical physical activity value willindicate that the user has poor physical fitness. Thus, the presentinvention not only facilitates accurate and effective blood glucoselevels for diabetics and other individuals who need to engage in such apractice, but can also facilitate general health awareness.

Although the invention has been explained in relation to its preferredembodiment, it is to be understood that many other possiblemodifications and variations can be made without departing from thespirit and scope of the invention as hereinafter claimed.

What is claimed is:
 1. A method of adaptively predicting blood-glucose level by collecting biometric and activity data with a user portable device, the method comprises the steps of: (A) providing at least one remote server, wherein the remote server manages a blood-glucose (BG) predictive formula generator and stores time-dependent user historical (TDUH) data; (B) providing at least one portable computing device, wherein the portable computing device is communicably coupled to the remote server; (C) providing a set of user activity levels and a set of current BG predictive formulas stored on the portable computing device, wherein each user activity level is associated with a corresponding formula within the set of current BG predictive formulas; (D) collecting user movement data with the portable computing device; (E) associating the user movement data with a specific activity level within the set of user activity levels with the portable computing device; (F) extrapolating a BG predictive model from the corresponding formula of the specific activity level over a pre-defined time block with the portable computing device; (G) displaying the BG predictive model through the portable computing device; (H) repeating steps (D) through (G) as a plurality of iterations, until the remote server updates the portable computing device with a set of new BG predictive formulas, wherein the user movement data for each iteration is compiled into time-dependent user movement (TDUM) data; (I) collecting time dependent user biometric (TDUB) data during the iterations with the portable computing device; (J) integrating the TDUM and the TDUB data into the TDUH data with the remote server; and (K) computing a set of new BG predictive formulas with the remote server by inputting the TDUH data into the BG predictive formula generator.
 2. The method as claimed in claim 1 comprises the steps of: providing a carriable monitoring device and a mobile computing device as the at least one portable computing device; executing step (D) through step (F) with the carriable monitoring device; sending the BG predictive model from the carriable monitoring device to the mobile computing device prior to step (G); executing step (G) with the mobile computing device; executing step (I) with the carriable monitoring device; sending the TDUM data from the carriable monitoring device to the mobile computing device prior to step (J); sending the TDUM data and the TDUB data from the mobile computing device to the remote server after step (I); and sending the new BG predictive formulas from the remote server to the carriable monitoring device through the mobile computing device after step (K).
 3. The method as claimed in claim 1 comprises the steps of: providing a single portable computing device as the at least one portable computing device; sending the TDUM data and the TDUB data from the single portable computing device to the remote server after step (I); and sending the new BG predictive formulas from the remote server to the single portable computing device after step (K).
 4. The method as claimed in claim 1 comprises the steps of: providing an internal movement sensor with the portable computing device; and collecting the user movement data with the internal movement sensor during step (D).
 5. The method as claimed in claim 1 comprises the steps of: providing a plurality of biometric sensors with the portable computing device; and receiving automatically-collected portions of the TDUB data with the plurality of biometric sensors during step (I).
 6. The method as claimed in claim 1 comprises the steps of: providing a user interface with the portable computing device; and receiving manually-inputted portions of the TDUB data with the plurality of biometric sensors during step (I).
 7. The method as claimed in claim 1, wherein the TDUB data includes information selected from a group consisting of: current BG level, food intake, insulin injection value, body mass index (BMI), pulse rate, blood oxygenation level, body impedance, and combinations thereof.
 8. The method as claimed in claim 1 comprises the steps of: providing a plurality of movement ranges stored on the portable computing device, wherein each movement range is associated to a corresponding activity level within the set of user activity levels; comparing the user movement data to each movement range with the portable computing device in order to identify a matching range from the plurality of movement ranges; and designating the corresponding activity level of the matching range as the specific activity level during step (E).
 9. The method as claimed in claim 1, wherein the BG predictive model is visually displayed as a graphical plot through the portable computing device.
 10. The method as claimed in claim 1, wherein: each of the plurality of iterations is executed at a pre-defined time interval; and the pre-defined time block is a multiple of the pre-defined time interval.
 11. The method as claimed in claim 1 comprises the steps of: (L) providing a preceding BG result; (M) applying a current counting variable into the corresponding formula for the specific activity level in order to calculate a current BG result with the portable computing device; (N) modifying the current BG result with the preceding BG result in order to calculate a predictive BG result with the portable computing device; (O) incrementing the current counting variable with the portable computing device; and (P) repeating steps (L) through (O) as a plurality of iterative calculations with the portable computing device in order to compile the predictive BG result from each iterative calculation into the BG predictive model.
 12. The method as claimed in claim 11 comprises the steps of: providing a pre-defined initial BG result; providing a first iterative calculation from the plurality of iterative calculations; and designating the pre-defined initial BG result as the preceding BG result for the first iterative calculation with the portable computing device.
 13. The method as claimed in claim 11 comprises the steps of: providing an arbitrary iterative calculation and a subsequent iterative calculation from the plurality of iterative calculations; and designating the predictive BG result for the arbitrary iterative calculation as the preceding BG result for the subsequent iterative calculation with the portable computing device.
 14. The method as claimed in claim 11 comprises the steps of: providing a plurality of polynomial terms for each current BG predictive formula, wherein each polynomial term includes a coefficient; and multiplying at least one of the polynomial terms by a scaling factor and an inverse of the current counting variable with the portable computing device in order to scale the corresponding formula for the specific activity level prior in between step (M) and (O).
 15. The method as claimed in claim 1, wherein the remote server executes a polynomial curve fitting process on the TDUH data in order to compute the set of new BG predictive formulas. 